The goal of our research is to add a new dimension to our understanding of the design criteria for renal clearance of dendrimer based nanostructures. We achieve this by gaining an understanding of how surface charge (SC) on dendrimers can be used to tune their pharmacological properties. Reports exist on how size affects the behavior of dendrimers in vivo, yet no extensive pharmacological study that keeps SC constant while varying the size and size constant while varying the SC exists. We aim to: Synthesize dendrimers of varying SC but constant size; Synthesize dendrimers of varying hydrodynamic radii (rh) and constant SC; and Determine their pharmacokinetics, biodistribution, and clearance pathway. Dendrimers have a core with repeat units. These repeat units grow from arms on the core. The number of arms is the core multiplicity (CM). The repeat unit anchors to the core at a point, and branches grow from that point. The number of these branches is the branch juncture multiplicity (BM), and the number of repeat units defines a generation (G). The number of surface groups (NSQ) is: NSG = CM* (BM)G. We use standard peptide chemistry to prepare dendrimers by convergent methods. Dendrons are prepared by linking the different repeat units from the surface (bifunctional Gd(lll)-Chelates) down to the attachment point. By using cores differing in CM and attaching dendrons prepared from repeat units differing in BM we prepared dendrimers with differing NSG but the same rh and differing rh but the same NSG. The rh is measured using light scattering and diffusion ordered NMR methods. We obtain the plasma clearance curves, [Tr]P(t), with MRI and direct blood sampling methods. From [Tr]P(t) we calculate the pharmacokinetic parameters and area under the curve (AUC). We will compare dendrimer values with those of a low molecular weight clinically approved Gd(lll)-chelate contrast agent. Renal clearance is compared to glomerular filtration rate by taking the dose found in the urine after t ~ "functional infinity" from normal animals and those treated with the anion tubular secretion inhibitor probenecid and dividing it by the AUC. Pharmacokinetic data is analyzed by fitting the [Tr]P(t) profiles with multicompartmental and non-compartmental models using ADAPT II. We use the maximum likelihood estimation and linear models for the variance of the additive errors. We also use Akaike's Information Criteria, Schwartz Criteria, estimated error of the model parameters, and residual analysis to select the model structure maximizing the fit accuracy and minimizing the number of parameters. This provides an understanding of the renal clearance design criteria for dendrimers used in diagnosis and therapy. This research literally adds a new dimension to our understanding of the design criteria needed for controlling and optimizing dendrimer based nanostructure kidney clearance. The knowledge gained is fundamental in making dendrimers for therapy and diagnosis with low toxicity and low whole body retention. [unreadable] [unreadable] [unreadable]